UBC Faculty Research and Publications

The Development of Intelligent Systems to Support Older Adults and Aging-in-Place Mihailidis, Alex 2009

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The Development of Intelligent Systems to Support Older Adults and Aging-in-PlaceAlex Mihailidis, Ph.D P.Eng.Department of Occupational Science & Occupational Therapy2The Problem Globally we are faced with the challenge of caring for an increasing number of older adults. Many individuals are living with diseases and impairments in addition to the normal aging process, for example dementia.Furthermore... There is a growing shortage of clinicians, nurses, and caregivers. In combination with the growing number of older adults, we are seeing  a drastic increase in workload and burden. This not only true in institutions but in the home as well!34The Result There is an increased need for new treatment options and solutions. Solutions need to be robust and easily scalable to meet the needs of this growing population. These solutions need to help support aging-in-place.5Overall Vision Our goal is to develop an intelligent home that can enable aging-in-place, no matter the context or activity.6Alex, don’t forget to use the soap.You have fallen.  Need Help?It’s time for your medication.Are you feeling okay?7Our Design Philosophy Develop for real-world contexts, using real-life problems and motivations. Involve the user from the start to the finish of the design process. Test new technologies as often as possible throughout the design process. Universal Design principles to accommodate all potential users (including caregivers).8Zero-Effort Technologies Development and testing of intelligent systems that are able to: • Automatically learn about the user and environment• Continuously collect data and information• Use contextual information to autonomously operate and make decisions This is all accomplished without any input from the user or caregiver. 9DataSharingThe COACH(ADL Cueing)HELPER(ERS)Anti-collisionWheelchairRobotic StrokeRehabilitationNursing PDAsBalanceAssess.Examples of Our Research10DataSharingThe COACH(ADL Cueing)HELPER(ERS)Anti-collisionWheelchairRobotic StrokeRehabilitationNursing PDAsBalanceAssess.Examples of Our Research11The COACHAn intelligent cognitive device that tracks a user through an ADL, providing cues when necessaryCognitive Orthosis for Assisting Activities in the HomeTheCOACH12Alzheimer’s Disease The number of people with AD worldwide is expected to grow from 18 million to 24 million by 2050. There is approximately one new case of AD every 7 seconds! 70 percent of people with Alzheimer’s and other dementias live at home.13Alzheimer’s Disease AD impairs explicit memory, resulting in difficulties in completing activities of daily living (ADL). The current solution is for a caregiver to constantly accompany the person and provide prompts, support, and monitoring. This is a very difficult and frustrating experience.14The COACHDigital Video CameraFlat Screen Monitor & Speakers15System Overview16Tracking17Planning The system’s belief monitoring and policy systems use AI to model the handwashing process and actions that may be taken. Represented using:• Level of impairment• Awareness • Responsiveness18Handwashing - Actions Do nothing: system waits Call caregiver: system calls for single step assistance Prompts:• Audio/video• Male voice• Preceded with reminder: “you’re washing your hands”• 3 levels of specificity19Prompt Specificity Minimal“Turn on the water” Moderate“Ed, pull up on the silver lever in front of you to turn the water on” Maximum“Ed, pull up on the silver lever in front of you to turn the water on”+20Example of UseFuture Work COACH@Home System• Multiple tasks• Easy installation• Aesthetics Intelligent dialogue management• Speech recognition• Multi-language, accents, vocal invariants2122Examples of Our ResearchDataSharingThe COACH(ADL Cueing)HELPER(ERS)Anti-collisionWheelchairRobotic StrokeRehabilitationNursing PDAsBalanceAssess.23DataSharingThe COACH(ADL Cueing)HELPER(ERS)Anti-collisionWheelchairRobotic StrokeRehabilitationNursing PDAsBalanceAssess.Examples of Our Research24The HELPER   An intelligent hands- free personal emergency response system to improve safety in the home25Overview of Falls Falls is one of the leading causes of morbidity and mortality in the elderly. One in three community-dwelling older adults experience at least one fall over the span of one year. One third of these falls occur in the home.26System ComponentsCameraMic array & speakers27System Schematic28Fall Detection29Dialogue Manager Once an adverse event has been detected the system must respond appropriately. Prompting and speech recognition will be used to have a dialogue with the user. Based on this dialogue, the system will determine the best course of action.30Response Actions Actions that can currently be taken by the system include:• Call a neighbor• Call a family member• Call an operator (e.g. Lifeline)• Call an emergency service (e.g. ambulance) Future actions may include prompting and reminders.31Example of UseCurrent / Future Research Focusing on the speech recognition / response system. Developing a speech database of older adult voices (healthy and not). Development of an adaptive dialogue that learns the preferences and needs of specific users.32Design Approach Revisited We have learned that applying our design philosophies and approach are extremely important in our successes. However, involving users has proven to be very costly and time consuming. In response, we are looking at new tools that we can use to make this approach more efficient.33Actors in Design We are starting to explore the use of actors in the design process. These actors would simulate different users so that we can test our systems more often during the design process. Building upon work by University of Dundee and classical use of patient simulation programs by medical students.34Actor simulations - Objectives The objectives of this pilot research were to investigate the following questions:• Can actors believably simulate older adults with dementia?• Can actors simulating older adults with dementia be used to optimize technologies before they go to clinical trials?35Actor simulations – Method Actors were trained using video footage from previous handwashing trials. Videos were recorded of older adults washing their hands:• 6 were actors portraying dementia (“simulated”)• 6 were people with dementia (“real”) 20 videos per person, for a total of 240 videos.36Actor simulations – Method Task 1: Participant randomly shown 20 segments (10 simulated and 10 real) and asked to rate on a scale from 1 (not at all) to 10 (very much):“Was the client behaving in a way a person with dementia would?”  Task 2: Participant shown 10 video pairs containing a simulated and a real trial and asked to choose which video was the actor.37Which one is the actor?38Actor simulations – Task 1Average score (between 1 and 10)Real / simulated dementia pair39Actor simulations – Task 2 65 % of the time the actor was correctly identified. “When making my choices, I felt…”• Very sure (0)• Somewhat sure (3)• Somewhat unsure (7)• Very unsure (1)4043Conclusions Non-traditional tools and techniques, such as AI, have the potential to make environments more usable and safe. Intended user must be kept in mind and involved often for successful outcomes. The potential exists to use actors to improve design process. In addition to technological challenges, development must focus on the social and ethical implications.42Acknowledgements Research has been supported through generous research grants and industrial support from:43ContactContact us:      Tel:   +1 (416) 946-8565Email:   alex.mihailidis@utoronto.ca  Web:  www.iatsl.org

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